Experimental Designs for Smart Manufacturing

Music City Center 

Many real-world manufacturing applications involve experiments with vector-valued inputs, where multiple parameters must be optimized simultaneously. Traditional design of experiments (DoE) methods often struggle with such high-dimensional, structured input spaces, calling for new approaches. In this work, we introduce branched orthogonal arrays (BOAs), a novel class of experimental designs tailored for vector-valued inputs. We present theoretical constructions for both regular and non-regular BOAs, along with efficient algorithmic generation methods. The proposed designs exhibit superior space-filling properties, stratification, and flexibility compared to conventional designs. We investigate their optimality criteria, including uniformity and orthogonality, and demonstrate their advantages in practical manufacturing settings. This work bridges the gap between advanced experimental design theory and complex manufacturing applications, offering a powerful tool for engineers and researchers working with vector-valued inputs.

Factorial factorial design, grouped orthogonal arrays, uncertainty quantification